library(tidyverse)
## ── Attaching packages ────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ───────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(gridExtra)
## 
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
## 
##     combine
surveys_complete <- read_csv("data/surveys_complete.csv")
## Parsed with column specification:
## cols(
##   record_id = col_double(),
##   month = col_double(),
##   day = col_double(),
##   year = col_double(),
##   plot_id = col_double(),
##   species_id = col_character(),
##   sex = col_character(),
##   hindfoot_length = col_double(),
##   weight = col_double(),
##   genus = col_character(),
##   species = col_character(),
##   taxa = col_character(),
##   plot_type = col_character()
## )
ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
  geom_point()

surveys_plot <- ggplot(data = surveys_complete, 
                       mapping = aes(x = weight, y = hindfoot_length))
surveys_plot + 
    geom_point()

ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
    geom_point(alpha = 0.1, aes(color = species_id))

Challenge

Scatter Plot of Weight Over Species_id

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) + 

    geom_point(aes(color = plot_type))

# This is not the best way to show the data.

Boxplot

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
    geom_boxplot()

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
    geom_jitter(alpha = 0.3, color = "tomato") +
    geom_boxplot(alpha = 0)

Challenges

ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
    geom_violin(alpha = 0) +
    geom_jitter(alpha = 0.3, color = "tomato") +
  scale_y_log10()

plot_id <- factor()
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = hindfoot_length)) +
    geom_violin(alpha = 0) +
    geom_jitter(alpha = 0.3) +
  geom_point(aes(color = plot_id))

  scale_y_log10()
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1

Plotting Time Series Data

yearly_counts <- surveys_complete %>%
  count(year, genus)
ggplot(data = yearly_counts, aes(x = year, y = n, color = genus)) +
    geom_line()

Pipe Operator

yearly_counts %>% 
    ggplot(mapping = aes(x = year, y = n, color = genus)) +
    geom_line()

yearly_counts_graph <- surveys_complete %>%
    count(year, genus) %>% 
    ggplot(mapping = aes(x = year, y = n, color = genus)) +
    geom_line()

yearly_counts_graph

### Faceting

ggplot(data = yearly_counts, aes(x = year, y = n)) +
    geom_line() +
    facet_wrap(facets = vars(genus))

Separated by Sex

 yearly_sex_counts <- surveys_complete %>%
                      count(year, genus, sex)
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_wrap(facets =  vars(genus))

# Facet by Sex and Genus

ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(rows = vars(sex), cols =  vars(genus))

# Facet By Rows

ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(rows = vars(genus))

# Facet By Columns

ggplot(data = yearly_sex_counts, 
       mapping = aes(x = year, y = n, color = sex)) +
  geom_line() +
  facet_grid(cols = vars(genus))

ggPlot Themes

 ggplot(data = yearly_sex_counts, 
        mapping = aes(x = year, y = n, color = sex)) +
     geom_line() +
     facet_wrap(vars(genus)) +
     theme_bw()

Challenge

yearly_weight <- surveys_complete %>%
                group_by(year, species_id) %>%
                 summarize(avg_weight = mean(weight))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(data = yearly_weight, mapping = aes(x=year, y=avg_weight)) +
   geom_line() +
   facet_wrap(vars(species_id)) +
theme_bw()

Customization

ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex)) +
    geom_line() +
    facet_wrap(vars(genus)) +
    labs(title = "Observed Genera Through Time",
         x = "Year of Observation",
         y = "Number of Individuals") +
    theme_bw()

theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
                        axis.text.y = element_text(colour = "grey20", size = 12),
                        strip.text = element_text(face = "italic"),
                        text = element_text(size = 16))
## List of 4
##  $ text       :List of 11
##   ..$ family       : NULL
##   ..$ face         : NULL
##   ..$ colour       : NULL
##   ..$ size         : num 16
##   ..$ hjust        : NULL
##   ..$ vjust        : NULL
##   ..$ angle        : NULL
##   ..$ lineheight   : NULL
##   ..$ margin       : NULL
##   ..$ debug        : NULL
##   ..$ inherit.blank: logi FALSE
##   ..- attr(*, "class")= chr [1:2] "element_text" "element"
##  $ axis.text.x:List of 11
##   ..$ family       : NULL
##   ..$ face         : NULL
##   ..$ colour       : chr "grey20"
##   ..$ size         : num 12
##   ..$ hjust        : num 0.5
##   ..$ vjust        : num 0.5
##   ..$ angle        : num 90
##   ..$ lineheight   : NULL
##   ..$ margin       : NULL
##   ..$ debug        : NULL
##   ..$ inherit.blank: logi FALSE
##   ..- attr(*, "class")= chr [1:2] "element_text" "element"
##  $ axis.text.y:List of 11
##   ..$ family       : NULL
##   ..$ face         : NULL
##   ..$ colour       : chr "grey20"
##   ..$ size         : num 12
##   ..$ hjust        : NULL
##   ..$ vjust        : NULL
##   ..$ angle        : NULL
##   ..$ lineheight   : NULL
##   ..$ margin       : NULL
##   ..$ debug        : NULL
##   ..$ inherit.blank: logi FALSE
##   ..- attr(*, "class")= chr [1:2] "element_text" "element"
##  $ strip.text :List of 11
##   ..$ family       : NULL
##   ..$ face         : chr "italic"
##   ..$ colour       : NULL
##   ..$ size         : NULL
##   ..$ hjust        : NULL
##   ..$ vjust        : NULL
##   ..$ angle        : NULL
##   ..$ lineheight   : NULL
##   ..$ margin       : NULL
##   ..$ debug        : NULL
##   ..$ inherit.blank: logi FALSE
##   ..- attr(*, "class")= chr [1:2] "element_text" "element"
##  - attr(*, "class")= chr [1:2] "theme" "gg"
##  - attr(*, "complete")= logi FALSE
##  - attr(*, "validate")= logi TRUE

Saving the Theme

grey_theme <- theme(axis.text.x = element_text(colour="grey20", size = 12, 
                                               angle = 90, hjust = 0.5, 
                                               vjust = 0.5),
                    axis.text.y = element_text(colour = "grey20", size = 12),
                    text=element_text(size = 16))
ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length)) +
    geom_boxplot() +
    grey_theme

Challenge

ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length, color = sex)) +
    geom_boxplot() +
  theme(axis.text.x = element_text(colour = "purple", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
                        axis.text.y = element_text(colour = "blue", size = 12),
                        strip.text = element_text(face = "italic"),
                        text = element_text(size = 16))

### Arranging and Exporting Plots

library(gridExtra)

spp_weight_boxplot <- ggplot(data = surveys_complete, 
                             aes(x = species_id, y = weight)) +
  geom_boxplot() +
  labs(x = "Species", 
       y = expression(log[10](Weight))) +
  scale_y_log10() + 
  labs()

spp_count_plot <- ggplot(data = yearly_counts, 
                         aes(x = year, y = n, color = genus)) +
  geom_line() + 
  labs(x = "Year", y = "Abundance")

grid.arrange(spp_weight_boxplot, spp_count_plot, ncol = 2, widths = c(4, 6))